Articles | Volume 21, issue 10
Nat. Hazards Earth Syst. Sci., 21, 3199–3218, 2021
https://doi.org/10.5194/nhess-21-3199-2021
Nat. Hazards Earth Syst. Sci., 21, 3199–3218, 2021
https://doi.org/10.5194/nhess-21-3199-2021

Research article 27 Oct 2021

Research article | 27 Oct 2021

Improving flood damage assessments in data-scarce areas by retrieval of building characteristics through UAV image segmentation and machine learning – a case study of the 2019 floods in southern Malawi

Lucas Wouters et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2020-417', Anonymous Referee #1, 31 Mar 2021
  • RC2: 'Review report for manuscript nhess-2020-417', Anonymous Referee #2, 07 Apr 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (07 Jun 2021) by Sven Fuchs
AR by Lucas Wouters on behalf of the Authors (04 Aug 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (09 Aug 2021) by Sven Fuchs
RR by Anonymous Referee #1 (25 Aug 2021)
RR by Anonymous Referee #2 (26 Aug 2021)
ED: Publish subject to minor revisions (review by editor) (01 Sep 2021) by Sven Fuchs
AR by Lucas Wouters on behalf of the Authors (11 Sep 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to technical corrections (14 Sep 2021) by Sven Fuchs
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Short summary
This research introduces a novel approach to estimate flood damage in Malawi by applying a machine learning model to UAV imagery. We think that the development of such a model is an essential step to enable the swift allocation of resources for recovery by humanitarian decision-makers. By comparing this method (EUR 10 140) to a conventional land-use-based approach (EUR 15 782) for a specific flood event, recommendations are made for future assessments.
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